Analysis of Regular Patterns in Un-Weighted Directed Graphs

Time evolving networks tend to have an element of regularity. This regularity is characterized by existence repetitive patterns in the data sequences graph metrics. As per our research, relevance such regular network has not been adequately explored. Such certain are indicative properties like popularity, activeness etc. which vital significance for any network. These closely indicated metrics - degree prestige, centrality and occurrence. In this paper, (a) improved mining algorithm used extract these sequences, (b) a methodology proposed quantitatively analyse behavior obtained patterns. To analyze behavior, quantification measure coined as "Sumscore" defined compare relative The ranked according their Sumscores insights then drawn upon it. efficacy method demonstrated experiments on two real world datasets.